{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
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       "      <td>第五食堂</td>\n",
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       "      <td>第五食堂</td>\n",
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       "      <td>182648</td>\n",
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       "      <td>182648</td>\n",
       "      <td>2019/4/10 21:18</td>\n",
       "      <td>第五食堂</td>\n",
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       "<p>204359 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CardNo             Date  Dept  hour  day\n",
       "0       181316  2019/4/20 20:17  第一食堂    20   20\n",
       "20      181316   2019/4/18 7:26  第一食堂     7   18\n",
       "21      181316   2019/4/19 7:32  第一食堂     7   19\n",
       "28      181316  2019/4/16 18:10  第一食堂    18   16\n",
       "33      181316    2019/4/4 7:31  第一食堂     7    4\n",
       "34      181316    2019/4/2 7:37  第一食堂     7    2\n",
       "35      181316    2019/4/2 7:37  第一食堂     7    2\n",
       "74      181316   2019/4/30 8:31  第一食堂     8   30\n",
       "75      181316    2019/4/8 7:30  第一食堂     7    8\n",
       "76      181316    2019/4/8 7:30  第一食堂     7    8\n",
       "88      181316   2019/4/6 12:16  第一食堂    12    6\n",
       "89      181316   2019/4/6 19:00  第一食堂    19    6\n",
       "90      181316   2019/4/6 12:16  第一食堂    12    6\n",
       "91      181316   2019/4/6 18:59  第一食堂    18    6\n",
       "92      181316   2019/4/6 12:14  第一食堂    12    6\n",
       "93      181316    2019/4/5 8:50  第一食堂     8    5\n",
       "119     181316  2019/4/20 20:14  第一食堂    20   20\n",
       "165     181318  2019/4/20 20:13  第一食堂    20   20\n",
       "166     181318  2019/4/20 20:18  第一食堂    20   20\n",
       "185     181318   2019/4/19 7:32  第一食堂     7   19\n",
       "192     181318  2019/4/15 21:04  第一食堂    21   15\n",
       "197     181318    2019/4/4 7:31  第一食堂     7    4\n",
       "198     181318    2019/4/2 7:37  第一食堂     7    2\n",
       "213     181318   2019/4/25 7:23  第一食堂     7   25\n",
       "216     181318   2019/4/30 8:31  第一食堂     8   30\n",
       "217     181318  2019/4/29 20:30  第一食堂    20   29\n",
       "218     181318  2019/4/29 20:31  第一食堂    20   29\n",
       "219     181318    2019/4/8 7:30  第一食堂     7    8\n",
       "220     181318   2019/4/30 8:32  第一食堂     8   30\n",
       "232     181318   2019/4/6 12:15  第一食堂    12    6\n",
       "...        ...              ...   ...   ...  ...\n",
       "232322  180483   2019/4/4 17:24  第五食堂    17    4\n",
       "232323  180483    2019/4/2 7:36  第五食堂     7    2\n",
       "232324  180483   2019/4/2 11:53  第五食堂    11    2\n",
       "232325  180483   2019/4/3 16:34  第五食堂    16    3\n",
       "232326  180483   2019/4/4 17:22  第五食堂    17    4\n",
       "232327  180483    2019/4/4 7:33  第五食堂     7    4\n",
       "232328  180483   2019/4/4 17:23  第五食堂    17    4\n",
       "232329  180483   2019/4/2 11:54  第五食堂    11    2\n",
       "232336  180483   2019/4/1 18:53  第五食堂    18    1\n",
       "232337  180483   2019/4/1 11:38  第五食堂    11    1\n",
       "232338  180483   2019/4/7 18:16  第五食堂    18    7\n",
       "232339  180483   2019/4/7 18:16  第五食堂    18    7\n",
       "232340  180483   2019/4/1 11:36  第五食堂    11    1\n",
       "232341  180483   2019/4/7 18:15  第五食堂    18    7\n",
       "232342  180483   2019/4/1 11:37  第五食堂    11    1\n",
       "232375  184185   2019/4/9 17:11  第五食堂    17    9\n",
       "232377  180570    2019/4/1 7:35  第五食堂     7    1\n",
       "232378  180570    2019/4/1 7:36  第五食堂     7    1\n",
       "232380  184261  2019/4/12 11:31  第五食堂    11   12\n",
       "232381  184261  2019/4/12 11:32  第五食堂    11   12\n",
       "232382  184261  2019/4/12 11:34  第五食堂    11   12\n",
       "232387  182648   2019/4/9 18:51  第五食堂    18    9\n",
       "232388  182648   2019/4/9 18:52  第五食堂    18    9\n",
       "232389  182648  2019/4/10 12:13  第五食堂    12   10\n",
       "232390  182648  2019/4/11 11:58  第五食堂    11   11\n",
       "232391  182648  2019/4/10 18:45  第五食堂    18   10\n",
       "232392  182648  2019/4/10 16:29  第五食堂    16   10\n",
       "232393  182648  2019/4/10 21:18  第五食堂    21   10\n",
       "232406  181687   2019/4/8 12:24  第五食堂    12    8\n",
       "232409  182852   2019/4/1 17:49  第五食堂    17    1\n",
       "\n",
       "[204359 rows x 5 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "data1=pd.read_csv('C:/Users/Q/Desktop/code/task1_1_1.csv')\n",
    "data1['Dept'].value_counts()\n",
    "datadiyi=data1[data1['Dept']=='第一食堂']\n",
    "datadier=data1[data1['Dept']=='第二食堂']\n",
    "datadisan=data1[data1['Dept']=='第三食堂']\n",
    "datadisi=data1[data1['Dept']=='第四食堂']\n",
    "datadiwu=data1[data1['Dept']=='第五食堂']\n",
    "data1=datadiyi.append(datadier).append(datadisan).append(datadisi).append(datadiwu)\n",
    "data1.to_csv('task2_1.csv',encoding='utf_8_sig',index=0)\n",
    "# data1\n",
    "data2=data1[['CardNo','Date','Dept']]\n",
    "data2['hour']=pd.DatetimeIndex(data2['Date']).hour\n",
    "data2['day']=pd.DatetimeIndex(data2['Date']).day\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "dupe=data2.duplicated(['CardNo','day','hour'])\n",
    "dupe=dupe[dupe]\n",
    "dupe.index\n",
    "data2.drop(dupe.index)\n",
    "diyi=sum(data2['Dept']=='第一食堂')\n",
    "dier=sum(data2['Dept']=='第二食堂')\n",
    "disan=sum(data2['Dept']=='第三食堂')\n",
    "disi=sum(data2['Dept']=='第四食堂')\n",
    "diwu=sum(data2['Dept']=='第五食堂')\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "labels=['第一食堂','第二食堂','第三食堂','第四食堂','第五食堂']\n",
    "\n",
    "sizes=[diyi,dier,disan,disi,diwu]\n",
    "matplotlib.rcParams['font.sans-serif']=['SimHei'] \n",
    "\n",
    "plt.pie(x = sizes, # 绘图数据\n",
    "    labels=labels)\n",
    "plt.axis('equal')\n",
    "plt.savefig('食堂占比.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def zaocan(a):\n",
    "    return 5<=a<=9\n",
    "def wucan(a):\n",
    "    return 9<a<=14\n",
    "def wancan(a):\n",
    "    return 14<=a<=22\n",
    "data2_zaocan=data2.loc[data1['hour'].apply(zaocan)]\n",
    "data2_wucan=data2.loc[data1['hour'].apply(wucan)]\n",
    "data2_wancan=data2.loc[data1['hour'].apply(wancan)]\n",
    "# print(data1_zaocan)\n",
    "# print(data1_wucan)\n",
    "# print(data1_wancan)\n",
    "zaocandiyi=sum(data2_zaocan['Dept']=='第一食堂')\n",
    "zaocandier=sum(data2_zaocan['Dept']=='第二食堂')\n",
    "zaocandisan=sum(data2_zaocan['Dept']=='第三食堂')\n",
    "zaocandisi=sum(data2_zaocan['Dept']=='第四食堂')\n",
    "zaocandiwu=sum(data2_zaocan['Dept']=='第五食堂')\n",
    "\n",
    "wucandiyi=sum(data2_wucan['Dept']=='第一食堂')\n",
    "wucandier=sum(data2_wucan['Dept']=='第二食堂')\n",
    "wucandisan=sum(data2_wucan['Dept']=='第三食堂')\n",
    "wucandisi=sum(data2_wucan['Dept']=='第四食堂')\n",
    "wucandiwu=sum(data2_wucan['Dept']=='第五食堂')\n",
    "\n",
    "wancandiyi=sum(data2_wancan['Dept']=='第一食堂')\n",
    "wancandier=sum(data2_wancan['Dept']=='第二食堂')\n",
    "wancandisan=sum(data2_wancan['Dept']=='第三食堂')\n",
    "wancandisi=sum(data2_wancan['Dept']=='第四食堂')\n",
    "wancandiwu=sum(data2_wancan['Dept']=='第五食堂')\n",
    "\n",
    "labels=['第一食堂','第二食堂','第三食堂','第四食堂','第五食堂']\n",
    "\n",
    "sizes1=[zaocandiyi,zaocandier,zaocandisan,zaocandisi,zaocandiwu]\n",
    "sizes2=[wucandiyi,wucandier,wucandisan,wucandisi,wucandiwu]\n",
    "sizes3=[wancandiyi,wancandier,wancandisan,wancandisi,wancandiwu]\n",
    "matplotlib.rcParams['font.sans-serif']=['SimHei'] \n",
    "plt.figure(figsize=(6,9))\n",
    "\n",
    "# plt.figure(figsize=(6,9))\n",
    "plt.pie(x = sizes1, # 绘图数据\n",
    "    labels=labels)\n",
    "plt.axis('equal')\n",
    "plt.title(\"早餐\")\n",
    "plt.savefig('食堂早餐占比.jpg')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(6,9))\n",
    "plt.pie(x = sizes2, # 绘图数据\n",
    "    labels=labels)\n",
    "plt.axis('equal')\n",
    "plt.title(\"午餐\")\n",
    "plt.savefig('食堂午餐占比.jpg')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(6,9))\n",
    "plt.pie(x = sizes3, # 绘图数据\n",
    "    labels=labels)\n",
    "plt.axis('equal')\n",
    "plt.title(\"晚餐\")\n",
    "plt.savefig('食堂晚餐占比.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# data2['day']=pd.DatetimeIndex(data2['Date']).day\n",
    "def xiuxi(a):\n",
    "    feigongzuori=[5,6,7,13,14,20,21,29,30]\n",
    "    return a in feigongzuori\n",
    "def gongzuo(a):\n",
    "    feigongzuori=[5,6,7,13,14,20,21,29,30]\n",
    "    return a not in feigongzuori\n",
    "feigong=data2.loc[data2['day'].apply(xiuxi)]\n",
    "gongzuori=data2.loc[data2['day'].apply(gongzuo)]\n",
    "t=[]\n",
    "plt.rcParams['font.sans-serif']='SimHei'   # 显示中文\n",
    "plt.rcParams['axes.unicode_minus']=False  # 显示负号\n",
    "\n",
    "for i in range(1,25):\n",
    "    t.append(sum(feigong['hour']==i)/len(feigong['hour']))\n",
    "plt.plot(range(1,25),t,'g-',label='非工作日')\n",
    "plt.xticks(range(1,25))\n",
    "tt=[]\n",
    "for i in range(1,25):\n",
    "    tt.append(sum(gongzuori['hour']==i)/len(gongzuori['hour']))\n",
    "plt.plot(range(1,25),tt,'b-',label='工作日')\n",
    "plt.xticks(range(1,25))\n",
    "plt.legend()\n",
    "plt.savefig('就餐峰值.jpg')\n",
    "#横轴是时间分布，纵轴是时间占比"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
